|Title:||Mining Health Social Media with Sentiment Analysis||Authors:||Yang F.-C.
|Keywords:||Health social media;Latent Dirichlet Allocation;Sentiment analysis||Issue Date:||2016||Journal Volume:||40||Journal Issue:||11||Source:||Journal of Medical Systems||Abstract:||
With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors. ? 2016, Springer Science+Business Media New York.
|Appears in Collections:||資訊管理學系|
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